Comorbidities affect risk of nonvariceal upper gastrointestinal bleeding

Comorbidities Affect Risk of Nonvariceal Upper Gastrointestinal Bleeding

COLIN JOHN CROOKS,1,2

JOE WEST,1,2

and TIMOTHY RICHARD CARD1,2

1

Division of Epidemiology and Public Health, The University of Nottingham, Nottingham City Hospital, Nottingham, UK and2

Nottingham Digestive Diseases Centre, National Institute for Health Research Biomedical Research Unit, Queen’s Medical Centre, Nottingham University Hospitals National Health Service Trust, Nottingham, UK

This article has an accompanying continuing medical education activity on page e18. Learning Objective: Upon

completion of this CME exercise, and reading of the associated paper, successful learners will be able to appraise how

multiple risk factors can contribute to upper gastrointestinal bleeding and recognize the large contribution of

comorbidity.

See Covering the Cover synopsis on page 1327.

BACKGROUND & AIMS:

The incidence of upper

gas-trointestinal bleeding (GIB) has not been reduced despite

the decreasing incidence of peptic ulcers, strategies to

eradicate

Helicobacter pylori

infection, and prophylaxis

against ulceration from nonsteroidal anti-inflammatory

drugs. Other factors might therefore be involved in the

pathogenesis of GIB. Patients with GIB have increasing

nongastrointestinal comorbidity, so we investigated

whether comorbidity itself increased the risk of GIB.

METHODS:

We conducted a matched case-control study

using linked primary and secondary care data collected in

England from April 1, 1997 through August 31, 2010.

Patients older than 15 years with nonvariceal GIB (n

⫽

16,355) were matched to 5 controls by age, sex, year, and

practice (n

⫽

81,636). All available risk factors for GIB

were extracted and modeled using conditional logistic

regression. Adjusted associations with

nongastrointesti-nal comorbidity, defined using the Charlson Index, were

then tested and sequential population attributable

frac-tions calculated.

RESULTS:

Comorbidity had a strong

graded association with GIB; the adjusted odds ratio for a

single comorbidity was 1.43 (95% confidence interval [CI]:

1.35–1.52) and for multiple or severe comorbidity was

2.26 (95% CI: 2.14%–2.38%). The additional population

attributable fraction for comorbidity (19.8%; 95% CI:

18.4%–21.2%) was considerably larger than that for any

other measured risk factor, including aspirin or

non-steroidal anti-inflammatory drug use (3.0% and 3.1%,

re-spectively).

CONCLUSIONS: Nongastrointestinal

co-morbidity is an independent risk factor for GIB, and

contributes to a greater proportion of patients with

bleeding in the population than other recognized risk

factors. These findings could help in the assessment of

potential causes of GIB, and also explain why the

incidence of GIB remains high in an aging population.

Keywords:

Etiology; Gastrointestinal Bleeding; Stomach.

H

elicobacter pylori infection, nonsteroidal

anti-in-flammatory medications (NSAIDs), and aspirin are

believed to be the main causes of nonvariceal upper

gas-trointestinal bleeding,

_{and with the discovery of proton}

pump inhibitors (PPIs) and

H pylori

eradication therapy,

the burden of peptic ulcer disease has been decreasing.

Despite this, upper gastrointestinal hemorrhage remains

the most common acute severe medical admission for

gastroenterology,

_{and its incidence in population-based}

studies remains virtually unchanged.

_{This suggests that}

other (previously unidentified) risk factors are

contribut-ing to its population burden.

Historically, nongastrointestinal comorbidity was

be-lieved to be associated with stress ulceration

_but,

cur-rently, the role of comorbidity in the etiology of

gastro-intestinal bleeding (GIB) is not recognized apart from in

severe illness; for example, sicker cirrhotic patients are

known to have an increased risk of variceal bleeding,

_and

sicker patients in intensive therapy units (ITUs) have an

increased risk of nonvariceal bleeding.

_{However, as the}

proportion of bleed patients with comorbidity has

in-creased during the last decade,

_{we wondered if exposure}

to less severe but chronic comorbidity could itself be

responsible for the persisting incidence of bleeding.

Out-side of ITU though, the effect of comorbidity has only

been assessed as a confounder in studies that focused on

the effect of medications on gastrointestinal bleeds.

Al-though these studies do support a role for comorbidity,

they do not allow us to understand whether it is an

important independent contributor to the persisting

bur-den of upper GIB.

We have therefore conducted a study aimed primarily at

assessing whether comorbidity might have an important

role in the etiology of upper GIB. To do this we have

conducted a case-control study and formed a model fully

corrected for known measured risk factors of upper GIB.

Abbreviations used in this paper: GIB, gastrointestinal bleeding; GPRD, General Practice Research Database; ITU, intensive therapy unit; NSAID, nonsteroidal anti-inflammatory drug; OR, odds ratio; PAF, pop-ulation attributable fraction; PPI, proton pump inhibitors.

©2013 by the AGA Institute 0016-5085/$36.00

http://dx.doi.org/10.1053/j.gastro.2013.02.040

CLINICAL

We have then calculated the additional explanatory effect

of adding comorbidity to our model to understand its

effect on bleeding incidence in the general population.

Methods

Study Design

We conducted a matched case control study.

Data

To provide the detailed longitudinal data and necessary power for this study, we have used the recently linked English Hospital Episodes Statistics data (secondary care data) and Gen-eral Practice Research Database (GPRD) (primary care data). Because of the comprehensive English primary care system, the population registered to the GPRD is representative of the

general English population.11 _{The data are subject to quality}

checks and a practice’s data are only used when they are of high enough quality to be used in research, at these times the data are

said to be “up to research standard.”12 _{The GPRD has been}

extensively validated for a wide range of diagnoses, with a mean

positive predictive value of 89%.13_{Ethical approval for this study}

was obtained from the Independent Scientific Advisory Com-mittee for Medicines and Healthcare products Regulatory Agency database research. Fifty-one percent of English practices in GPRD have consented to record level linkage of their popu-lation to Hospital Episodes Statistics. This records all hospital admissions from the population registered to one of the linked primary care practices contributing to the GPRD. For this study, the linked dataset was available between April 1, 1997 and August 31, 2010.

Case Definition

We have previously published the codes and methods

used to define upper gastrointestinal bleeds in this study.14_In

brief, we selected as exposed all patients with a first nonvariceal upper gastrointestinal bleed. A bleed was defined by a specific code for an upper gastrointestinal nonvariceal bleed in either primary or secondary care who had a supporting code in the linked dataset (defined as a likely symptom, cause, therapy, investigation, or outcome of upper gastrointestinal hemor-rhage). Variceal bleeds or nonspecific gastrointestinal bleed codes with either a lower gastrointestinal diagnosis or procedure were excluded. Further exclusions were temporary patients (pa-tients not registered permanently at a GPRD primary care prac-tice, who might just be visiting the area of the practice briefly, and who are therefore not part of the GPRD’s underlying pop-ulation), children younger than 16 years old, cases with invalid date codes, or cases outside the up-to-research-standard ob-served time periods. Patients were required to be registered with the primary care practice for at least 3 months before an upper gastrointestinal bleed event to avoid including prevalent cases that might have been coded at the initial registration consulta-tion. Only the first event for each patient was included. We have previously demonstrated that this selection strategy minimizes

selection bias in studies of upper GIB in these data.14_A

second-ary analysis was then stratified by whether the defining bleed code or supporting code specifically referred to a peptic ulcer

(Read codes J11 to J14 orInternational Classification of Diseases, 10th

Revision codes K25–K28). The Read codes had high positive

predictive values (⬎95%) for peptic ulcers and upper

gastroin-testinal complications when validated in English primary care

routine records.15,16

Matched Controls

Each case was age (⫾5 years) and sex matched without

replacement to 5 controls selected randomly who were alive at the time of the gastrointestinal bleed and registered to the same primary care practice. Controls were required to have been reg-istered with the primary care practice for at least 3 months before the match date to be consistent with the definition for cases.

Exposures

Potential final common causal pathways of an upper gastrointestinal bleed were defined a priori for erosions/ulcer-ation, varices, angiodysplasia, fistula/trauma and coagulopathy, and code lists derived for diagnoses and medications that might be associated with each pathway based on published literature (Figure 1). Although variceal bleeds were excluded from the cases and controls, cirrhosis itself was included as a risk factor, as cirrhotic patients can have nonvariceal bleeds. Medication risk factors were included if there was a coded prescription within the year before the admission. Exposures coded within 2 months of the admission date were excluded to avoid identifying events and prescriptions related to the actual bleed event. PPIs were included as an indicator of physicians’ judgement of the risk of upper gastrointestinal hemorrhage that was not captured by other measured risk factors. Alcohol consumption was classified as either nondrinker, alcohol mentioned, ex–alcohol depen-dency, alcohol excess, alcohol complications, and missing. Smoking was classified as never smoked, current smoker, ex-smoker, and missing. Cirrhosis was classified as uncomplicated, with varices, with ascites, or with encephalopathy or liver failure coded. All other exposures were binary variables.

Comorbidity

Comorbidity was defined using the Charlson Index.17

This is a well-validated weighted comorbidity score derived from unselected hospital admissions that predicts 1-year mortality after hospital discharge. It has since been used in many contexts and has repeatedly measured the burden of comorbidity reliably. The original article demonstrated a graded increase in the risk in mortality associated with an increase in total score. The different comorbidities were assigned weights of 1, 2, 3, and 6, depending on their association with mortality. Where a graded effect was observed within a disease, for example, in diabetes or malig-nancy, these diseases were further stratified according to their severity. The conditions included in the original score (in order of weighting) were myocardial infarction, congestive heart fail-ure, peripheral vascular disease, cerebrovascular disease, demen-tia, chronic pulmonary disease, connective tissue disease, peptic ulcer disease, mild liver disease, diabetes, hemiplegia, moderate or severe renal disease, diabetes with end organ damage, leuke-mia, lymphoma, moderate or severe liver disease, metastatic solid tumor, and acquired immunodeficiency syndrome. For our study, any codes already used to define risk factors of upper GIB inFigure 1were excluded when calculating the index, ie, peptic ulcer and cirrhosis codes. For clarity in reporting in the tables,

the index was summarized as no comorbidity (Charlson Index⫽

0), single comorbidity (Charlson Index⫽ 1), and multiple or

severe comorbidity (Charlson Index⫽2).

Analysis

Unadjusted analysis. Unadjusted odds ratios (ORs) were calculated for each exposure using conditional logistic regression to allow for the matched study design.

CLINICAL

Multivariable analysis. Adjusted ORs for each expo-sure of interest were calculated with conditional logistic regres-sion adjusting for all exposures in addition to age, PPI use, and previous gastrointestinal procedures. As calendar year, sex, and primary care practice were precisely matched on in the controls, it was not necessary to include them in the model. Comorbidity was added last, and its association with bleeding tested using a likelihood ratio test. The variance inflation factor (a measure of the increase in model variance due to correlation between vari-ables) was calculated for each exposure of interest to assess the effect of correlation between variables. All exposures with a

variance inflation factor⬎5 were excluded from the final

con-ditional logistic regression model.18_{The final model was then}

stratified into cases with a recording of peptic ulcer and those without.

Sequential (or Extra) Population Attributable

Fractions

Sequential (or extra) population attributable fractions (PAFs) were calculated for each exposure, using the prevalence among the cases and the respective coefficients from the

condi-tional logistic regression model.19_{Sequential PAFs differ from}

the standard adjusted PAFs that are usually presented. They are calculated by estimating the additional proportion of cases at-tributable to each exposure, after removing the proportion of cases already attributed to the combined effect of all other exposures in the model. The final model was then stratified into cases with a recording of peptic ulcer and those without. All analysis was performed using Stata software, version 12 (Stata-Corp LP, College Station, TX).

Sensitivity Analyses

Previous studies of risk factor medications, such as

NSAIDs,20_{have been conducted in study populations that}

ex-cluded patients with known risk factors for GIB. To allow comparisons with these, we re-estimated the crude ORs for each of the risk factor medications after excluding any cases and their controls with nonmedication bleed risk factors. To assess the effect of the choice of the exposure exclusion time window before the bleed event on the effect of NSAIDs, we also re-estimated a model that included NSAID use up to 30 days before the index date.

Two additional sensitivity analyses were performed to assess the effect of potential under-reporting. First the analysis was restricted to those older than 65 years old and who were eligible for free prescriptions, to assess the effect of potential under-reporting of nonprescribed NSAID use. Secondly, multiple im-putation was used to re-estimate the association with comorbid-ity by imputing missing values for alcohol and smoking status. Alcohol and smoking were categorised as binary exposures of excess alcohol or current smoking to fit the logistic regression imputation model. All previously extracted exposures were used in the imputation model with addition of the socioeconomic status, and 20 sets of imputations were calculated. Socioeconomic status was measured by the Index of Multiple Deprivation quintiles ob-tained from linked Office of National Statistics data.

Finally, to assess the effect of using the aggregated and weighted Charlson Index, the model was re-estimated to assess the effect of the individual component comorbidities from the Charlson Index.

Figure 1. Risk factors for upper GIB. SSRI, selective serotonin reuptake inhibitor.

CLINICAL

Results

Cases and Matching

There were 16,355 unique cases identified with a

first nonvariceal bleed; 13,372 with specific code in

Hospital Episodes Statistics, 10,938 with a specific code

in GPRD, and 7955 with a specific code in both

data-sets. There were 16,304 (99.7%) cases matched to 5

controls each and only 8 cases (0.05%) were not

matched to any controls. Median observed time before

admission for cases was 7.4 years (interquartile range,

3.4 –11.5) compared with 7.5 years (interquartile range,

3.5–11.5) for controls.

Unadjusted Analysis

Table 1

shows the proportion of cases and controls

with each exposure. As expected, aspirin and NSAIDs were

the most frequently prescribed risk factor medications,

and peptic ulcer and gastritis/duodenitis/esophagitis were

the most frequent risk factor diagnoses. All a priori risk

factors were associated with upper GIB. Peptic ulcers were

coded as a diagnosis within the linked data in 4,823

patients (29% of cases). The exposures stratified by coding

of peptic ulcer are shown in

Supplementary Table 1

.

Multivariable Analysis and PAFs

There was strong evidence for an association

be-tween the nongastrointestinal Charlson Index and upper

GIB after adjusting for all measured risk factors (single

comorbidity adjusted OR

⫽

1.43; 95% CI: 1.35–1.52;

mul-tiple or severe comorbidity adjusted OR

⫽

2.26; 95% CI:

2.14 –2.38;

P

⬍

.001 likelihood ratio test).

Table 2

shows

the adjusted ORs from the final model for each exposure.

We found the largest association with a bleed was with a

previous Mallory-Weiss syndrome, which reflects the

in-herent risk of bleeding in recurrent vomitters. The

ables for angiodysplasia and dialysis had the highest

vari-ance inflation factors, 1.48 and 2.35, respectively. As both

of these were less than the a priori threshold of 5, all

exposures were included in the final conditional logistic

regression model.

Stratifying this model demonstrated similar

associa-tions with comorbidity, whether or not peptic ulcer

cod-ing was present, and slightly higher associations for a

peptic ulcer with exposure to previous peptic ulcers,

NSAID, or aspirin use (

Table 3

). Associations with other

risk factors were higher in the nonpeptic ulcer cohort.

The proportion of cases attributable in the population

to the combined effect of all available measured exposures

was 48%, not including the effect of nongastrointestinal

comorbidity. The additional proportion of cases

attribut-able to nongastrointestinal comorbidity (or the sequential

PAF) was 20%, and this was higher in magnitude than for

any other measured exposure (

Table 4

). The next largest

PAFs were 3% for aspirin and NSAID use.

The PAF for comorbidity associated with peptic ulcer

bleeds was slightly lower than that for nonulcer bleeds

(18% vs 21%), with a higher contribution from previous

peptic ulcer bleeds and aspirin and NSAIDs (

Table 5

). In

contrast, for nonulcer bleeds, the PAF was slightly

in-creased for gastrointestinal cancer, alcohol,

anticoagu-lants, and selective serotonin reuptake inhibitors.

Table 1. Proportion of Cases and Controls Exposed 2 Months Before Bleed Date or Match Date

Controls,

n

Exposed, %

Cases,

n

Exposed, %

Charlson Indexa

No comorbidity 30,194 37.0 3440 21.0 Single comorbidity 18,714 22.9 3222 19.7 Multiple or severe 32,728 40.1 9693 59.3 Gastrointestinal

Cirrhosis—none coded

81,385 99.7 16,004 97.9

Cirrhosis only 65 0.1 63 0.4 Cirrhosis—varices 62 0.1 65 0.4 Cirrhosis—ascites 86 0.1 172 1.1 Cirrhosis—

encephalopathy

38 0.0 51 0.3

Gastritis, duodenitis, or esophagitis

7904 9.7 3051 18.7

Peptic ulcer 3830 4.7 1852 11.3

Helicobacter pylori 1964 2.4 609 3.7

Angiodysplasia 14 0.0 6 0.0 Mallory-Weiss

syndrome

34 0.0 96 0.6

Crohn’s disease 222 0.3 114 0.7 GI cancer 2494 3.1 1174 7.2 Lifestyle

Alcohol—not coded 61,536 75.4 11,026 67.4 Alcohol—nondrinker 1485 1.8 375 2.3 Alcohol—ex-drinker 176 0.2 64 0.4 Alcohol—mentioned 4317 5.3 977 6.0 Alcohol—over limits 14,073 17.2 3763 23.0 Alcohol—complications 49 0.1 150 0.9 Smoking—not coded 51,751 63.4 9187 56.2 Smoking—nonsmoker 11,666 14.3 2332 14.3 Smoking—ex-smoker 4075 5.0 888 5.4 Smoking—passive 5574 6.8 1455 8.9 Smoking—current 8570 10.5 2493 15.2 Medications

Aspirin 18,079 22.1 5392 33.0 NSAIDs 12,722 15.6 3820 23.4 COX II inhibitors 1687 2.1 605 3.7 Clopidogrel 1297 1.6 668 4.1 Oral steroids 4135 5.1 1578 9.6 Anticoagulants 3799 4.7 1617 9.9 SSRIs 4813 5.9 2025 12.4 Other diagnoses

Aortic stenosis 782 1.0 350 2.1 Repair of AAA 307 0.4 115 0.7 Dialysis 70 0.1 88 0.5 Confounders

Previous upper GI procedure

10,471 12.8 3438 21.0

PPI 10,909 13.4 4585 28.0 Age (median and

interquartile range)

73 57–82 72 57–81

AAA, Abdominal aortic aneursym; SSRI, selective serotonin reuptake inhibitors.

a_{Nongastrointestinal comorbidity is catogorized as: Charslon Index⫽0}

is no comorbidity, Charlson Index ⫽ 1 single or comorbidity, and Charlson Index⫽2 is multiple or severe comorbidity.

CLINICAL

Sensitivity Analyses

The crude ORs were re-estimated for medications

after excluding cases with nonmedication risk factors and

these are shown in

Supplementary Table 2

. NSAID use

was strongly associated with bleeding, with an OR of 1.67,

and this increased to 2.80 with the exclusion of

nonmedi-cation risk factors. The corresponding adjusted ORs

associated with NSAIDs were 1.59 with nonmedication

risk factors included and 1.73 without. Altering the

exposure exclusion window for NSAIDs to 30 days

rather than 60 days before the bleed slightly increased

the effect of NSAIDS, but had only a minimal effect on

the other results, including comorbidity (see

Supple-mentary Table 3

).

Restricting the analysis to those older than 65 years old

increased the proportion of cases attributable to the

com-bined effect of all exposures from 48% to 63%, and reduced

the additional proportion of cases attributable to

nongas-trointestinal comorbidity from 19.8% to 16.1%.

Re-estimat-ing the model usRe-estimat-ing multiple imputation for missRe-estimat-ing

alco-hol and smoking status (modeled as binary exposures)

slightly reduced the PAF associated with comorbidity from

22.9% to 22.4%, but when alcohol and smoking status were

omitted from the model, the PAF was almost unaltered at

22.2%. Finally, the full model was re-estimated for each

component of the Charlson Index (

Table 6

). The

contribu-tion of these individual comorbidities was minimal in

com-parison with their combined weighted effect in the Charlson

Index in the main analysis.

Discussion

This study has demonstrated that a combined

measure of nongastrointestinal comorbidity is a

signifi-cant independent predictor of upper GIB, even after

ac-counting for all other recognized and measured risk

fac-tors. In addition, it explained a greater proportion of the

burden of bleeding than any other risk factor in the

population. The effect of this combined measure of

non-gastrointestinal comorbidity was far in excess of that

which would be expected from its constituent diseases.

The association of comorbidities with upper GIB has

been studied previously, but only in smaller secondary

care surveys with comorbidity as a confounder and not as

the primary exposure. We searched PubMed using

vari-ants of comorbidity, etiology, causality, risk factors, and

gastrointestinal hemorrhage; however, no studies were

identified that set out to address the question of our

article. Studies were most frequently designed to measure

the association of a single medication while adjusting for

any confounding by comorbidity.

Two studies assessed a larger range of medications in

cross-sectional hospital-based surveys.

_{First, Weil found}

that only 2 comorbidities, heart failure and diabetes,

con-tributed to upper GIB with adjusted PAFs of 5% and 4%,

respectively.

_{However, the study was retrospective, and}

with

⬍

1000 cases limiting its power. In contrast to the “extra

PAF” we calculated, the adjusted PAFs in their article

calcu-lated the effect of each exposure in a pseudo-population

with no other risk factors present, potentially overestimating

the effect in the general population, in which a case can be

caused by many risk factors. The second comparable paper

of Gallerani et al found an association with comorbidity and

a similar 2-fold increase in risk in those exposed to NSAIDs

to what we found in our peptic ulcer cohort.

_{However, it}

was also a retrospective survey– based study potentially

sub-ject to recall bias, and had

⬍

1000 cases. Furthermore, the

authors did not separate out gastrointestinal comorbidity

Table 2. Adjusted Model for Nonvariceal Upper

Gastrointestinal Bleeding All Cases (Age, Year, Practice, and Sex Matched)

Adjusted OR

Lower 95% CI

Upper 95% CI

Charlson Index

No comorbidity 1.00 1.00 1.00 Single comorbidity 1.43 1.35 1.52 Multiple or severe 2.26 2.14 2.38 Gastrointestinal

Cirrhosis—none 1.00 1.00 1.00 Cirrhosis only 3.89 2.61 5.77 Cirrhosis—varices 3.75 2.51 5.61 Cirrhosis—ascites 5.96 4.46 7.96 Cirrhosis—encephalopathy 5.05 3.14 8.10 Gastritis, duodenitis, or

esophagitis

1.46 1.39 1.55

Peptic ulcer 2.11 1.98 2.26

Helicobacter pylori 0.96 0.86 1.07

Angiodysplasia 1.67 0.58 4.80 Mallory-Weiss syndrome 12.39 8.16 18.82 Crohn’s disease 2.19 1.71 2.81 GI cancer 2.13 1.97 2.31 Lifestyle

Alcohol—not 1.00 1.00 1.00 Alcohol—nondrinker 1.25 1.10 1.42 Alcohol—ex-drinker 1.39 1.01 1.92 Alcohol—mentioned 1.05 0.96 1.14 Alcohol—over limits 1.42 1.35 1.49 Alcohol—complications 9.33 6.48 13.44 Smoking—not 1.00 1.00 1.00 Smoking—non-smoker 0.97 0.92 1.04 Smoking—ex-smoker 0.94 0.86 1.02 Smoking—passive 1.03 0.95 1.11 Smoking—current 1.29 1.22 1.37 Medications

Aspirin 1.50 1.43 1.57 NSAIDs 1.59 1.52 1.66 COX II inhibitors 1.52 1.37 1.69 Clopidogrel 1.74 1.57 1.94 Oral steroids 1.38 1.29 1.48 Anticoagulants 1.94 1.81 2.08

SSRIs 1.72 1.62 1.83

Other diagnoses

Aortic stenosis 1.58 1.38 1.82 Repair of AAA 1.29 1.02 1.64 Dialysis 3.59 2.55 5.05 Confounders

Previous upper GI procedure 1.10 1.04 1.15

PPI 1.59 1.52 1.67

Age 1.09 1.08 1.10

AAA, Abdominal aortic aneurysm; SSRI, selective serotonin reuptake inhibitors.

CLINICAL

from nongastrointestinal comorbidity and used hospital

controls, therefore limiting comparisons with our

popula-tion-based study.

Other studies assessed higher alcohol intake,

_{H pylori,}

smoking,

_{acute renal failure,}

_{and acute myocardial}

infarc-tion

_{and found associations with upper GIB. But these}

studies were in small selected hospitalised cohorts (n

⬍

1000

bleeds) with limited assessments of individual comorbidity

and no measure of their PAFs.

Our study has a number of important strengths when

compared with these previous works because we set out

specifically to assess the degree to which

nongastrointes-tinal comorbidity predicts nonvariceal upper GIB after

removing the effects of all the available known risk factors

in a much larger general population. In addition, we used

a method of defining cases and exposures that utilized

information from both primary and secondary care,

thereby maximizing the evidence supporting each case

while not excluding severe events.

_{Furthermore, due to}

the comprehensive coverage of the English primary care

system, our study’s results are likely to be generalizable to

the whole English population and, we believe, further

Table 3. Adjusted Model for Nonvariceal Upper Gastrointestinal Bleeding Stratified by Coding of Peptic Ulcer (Age, Year, Practice, and Sex Matched)

Peptic ulcer Nonpeptic ulcer

Adjusted OR Lower 95% CI Upper 95% CI Adjusted OR Lower 95% CI Upper 95% CI

Charlson Indexa

No comorbidity 1.00 1.00 1.00 1.00 1.00 1.00 Single comorbidity 1.45 1.30 1.62 1.42 1.33 1.52 Multiple or severe 2.28 2.06 2.52 2.27 2.13 2.42 Gastrointestinal

Cirrhosis—none 1.00 1.00 1.00 1.00 1.00 1.00 Cirrhosis only 3.98 2.03 7.80 3.80 2.30 6.27 Cirrhosis—varices 2.33 0.92 5.94 4.15 2.63 6.54 Cirrhosis—ascites 4.67 2.63 8.29 6.85 4.85 9.65 Cirrhosis—encephalopathy 3.16 1.39 7.20 6.66 3.70 12.01 Gastritis, duodenitis, or esophagitis 1.22 1.10 1.36 1.58 1.48 1.68 Peptic ulcer 4.36 3.92 4.85 1.37 1.25 1.49

Helicobacter pylori 1.04 0.85 1.27 0.94 0.83 1.06

Angiodysplasia 1.71 0.16 18.64 1.49 0.44 5.00 Mallory Weiss syndrome 3.75 1.43 9.84 16.54 10.23 26.77 Crohns disease 1.18 0.68 2.05 2.65 1.99 3.54

GI cancer 1.45 1.23 1.69 2.45 2.23 2.70

Lifestyle

Alcohol—not 1.00 1.00 1.00 1.00 1.00 1.00

Alcohol—nondrinker 1.14 0.89 1.47 1.30 1.11 1.51 Alcohol—ex-drinker 1.58 0.89 2.81 1.30 0.88 1.93 Alcohol—mentioned 1.02 0.87 1.20 1.04 0.94 1.16 Alcohol—over limits 1.34 1.22 1.47 1.45 1.36 1.54 Alcohol—complications 3.88 1.70 8.87 11.85 7.76 18.10

Smoking—not 1.00 1.00 1.00 1.00 1.00 1.00

Smoking—nonsmoker 0.99 0.88 1.11 0.96 0.90 1.04 Smoking—ex-smoker 0.96 0.82 1.13 0.92 0.83 1.03 Smoking—passive 0.95 0.82 1.09 1.06 0.97 1.16 Smoking—current 1.35 1.21 1.51 1.28 1.19 1.37 Medications

Aspirin 1.69 1.56 1.82 1.42 1.34 1.50

NSAIDs 2.21 2.04 2.39 1.37 1.29 1.45

COX II inhibitors 1.81 1.51 2.17 1.42 1.24 1.62

Clopidogrel 2.04 1.68 2.48 1.70 1.49 1.93

Oral steroids 1.31 1.16 1.49 1.40 1.29 1.51 Anticoagulants 1.67 1.47 1.90 2.10 1.94 2.28

SSRIs 1.47 1.30 1.66 1.84 1.71 1.97

Other diagnoses

Aortic stenosis 1.79 1.41 2.26 1.46 1.23 1.75 Repair of AAA 1.33 0.87 2.04 1.27 0.95 1.68

Dialysis 5.56 2.95 10.48 2.92 1.94 4.41

Confounders

Previous upper GI procedure 0.88 0.80 0.98 1.20 1.13 1.28

PPI 0.82 0.74 0.91 2.01 1.90 2.13

Age 1.10 1.08 1.11 1.09 1.08 1.10

AAA, Abdominal aortic aneursym; SSRI, selective serotonin reuptake inhibitors.

a_{Nongastrointestinal comorbidity is catogorized as: Charlon Index⫽0 is no comorbidity, Charlson Index⫽1 single or comorbidity, and Charlson}

Index⫽2 is multiple or severe comorbidity.

CLINICAL

afield. The linked dataset used for our study remained

representative of the GPRD overall, as whole practices

rather than individual patients declined or consented to

the linkage. Consequently, we were able to estimate the

additional attributable fraction for comorbidity in the

English population that was not already attributable to

other risk factors.

As our study was one of the first to assess the effect of the

burden of comorbidity as a risk factor for upper GIB, no

measure of comorbidity had been specifically validated for

this purpose. We decided to use the Charlson Index because

it is a well-validated score for measuring comorbidity in

many different contexts. Other comorbidity scores that

could be used, such as the Elixhauser Index or a simple

counts of diagnoses, have been used and validated less

fre-quently and in fewer contexts. In addition, some of the other

scores also include other outcomes, such as financial cost,

which are not necessarily a measure of the severity of disease.

The Charlson Index was therefore selected as the most

ap-propriate comorbidity score for our study.

We do need to consider alternative explanations for our

observed association of comorbidity with upper GIB. A

po-tential weakness of our study is the inevitably imperfect data

on some recognized risk factors that might have caused us to

underestimate their importance. The GPRD contains

com-prehensive recording of all available diagnoses and

prescrip-tions. However, under-reporting is likely to have occurred for

H pylori

infection, NSAID use, alcohol, and smoking. In the

case of

H pylori, there was inevitable under-reporting because

there was no population screening. However, if the

under-reporting of

H pylori

infection was to explain our study’s

findings, it would have to be strongly associated with

co-morbidity, and the evidence for this is conflicting and

un-derpowered.

_{In studies of ischemic heart disease, for}

which there is the largest body of evidence, any significant

association with

H pylori

was minimal after adjustments for

confounding.

_{In our study, the apparent protective effect of}

_H

pylori

after adjustments for confounding was not surprising

because

H pylori

will have been eradicated when found.

Table 4. Sequential Population Attributable Fractions for Nonvariceal Upper Gastrointestinal Hemorrhage (All Cases)

Sequential population attributable fractionsa,b

% 95% CI

Nongastrointestinal comorbidity 19.80 18.43 to 21.18 Gastrointestinal

Cirrhosis 0.49 0.41 to 0.57 Gastritis, duodenitis or esophagitis 1.98 1.66 to 2.30 Peptic ulcer 2.05 1.81 to 2.28

Helicobacter pylori ⫺0.04 ⫺0.15 to 0.08

Angiodysplasia 0.01 ⫺0.01 to 0.02 Mallory-Weiss syndrome 0.29 0.22 to 0.37 Crohn’s disease 0.14 0.08 to 0.19 GI cancer 1.11 0.96 to 1.27 Lifestyle

Alcohol use 2.89 2.39 to 3.39 Smoking 0.83 0.27 to 3.42 Medications

Aspirin 2.95 2.54 to 3.36 NSAIDs 3.07 2.72 to 3.42 COX II inhibitors 0.33 0.23 to 0.44 Clopidogrel 0.34 0.26 to 0.43 Oral steroids 0.59 0.44 to 0.74 Anticoagulants 1.19 1.04 to 1.35 SSRIs 1.58 1.36 to 1.80 Other diagnoses

Aortic stenosis 0.16 0.10 to 0.22 Repair of aorta 0.03 0.00 to 0.06 Dialysis 0.07 0.04 to 0.09

SSRI, selective serotonin reuptake inhibitors.

a_{Age, year, practice, and sex matched and adjusted for PPI use,}

previous upper gastrointestinal procedures, and age.

b_{The estimate in each row are calculated separately conditional on all}

the other variables in the model. They should therefore not be inter-preted as summing over the column to 100%. Sequential PAF esti-mates the additional proportion of nonvariceal bleeding cases attrib-utable to each risk factor after cases attribattrib-utable to all the other risk factors in the model have been removed.

Table 5. Sequential Population Attributable Fractions for Nonvariceal Upper Gastrointestinal Hemorrhage Strafied by Coding for Peptic Ulcer

Sequential population attributable fractions,a,b_%

Peptic ulcer

Nonpeptic ulcer

Nongastrointestinal comorbidity 18.44 20.50 Gastrointestinal

Cirrhosis 0.32 0.57

Gastritis, duodenitis, or esophagitis

0.69 2.74

Peptic ulcer 5.31 0.69

Helicobacter pylori 0.05 ⫺0.07

Angiodysplasia 0.01 0.00 Mallory-Weiss syndrome 0.06 0.39 Crohn’s disease 0.02 0.19

GI cancer 0.35 1.48

Lifestyle

Alcohol use 1.93 3.30

Smoking 0.80 0.81

Medications

Aspirin 3.99 2.42

NSAIDs 5.40 2.00

COX II inhibitors 0.47 0.28 Clopidogrel 0.38 0.35 Oral steroids 0.36 0.66 Anticoagulants 0.78 1.41

SSRIs 0.74 2.02

Other diagnoses

Aortic stenosis 0.22 0.12 Repair of aorta 0.02 0.03

Dialysis 0.09 0.05

SSRI, selective serotonin reuptake inhibitors.

a_{Age, year, practice, and sex matched and adjusted for PPI use,}

previous upper gastrointestinal procedures and age.

b_{The estimate in each row are calculated separately conditional on all}

the other variables in the model. They should therefore not be inter-preted as summing over the column to 100%. Sequential PAF esti-mates the additional proportion of nonvariceal bleeding cases attrib-utable to each risk factor after cases attribattrib-utable to all the other risk factors in the model have been removed.

CLINICAL

NSAID use might also have been under-reported, as

NSAIDs can be bought over the counter from a pharmacy

without a prescription, potentially explaining the low

asso-ciation between NSAIDs and bleeding in our study

com-pared with a previous meta-analysis.

_{However, we had}

higher recorded NSAID use than was reported in a recent

national audit,

_{and the studies used in the meta-analysis}

excluded patients with other known GIB risk factors.

When we made the same exclusions in our study (

Supple-mentary Table 2

), or restricted to peptic ulcers, the

associa-tion of bleeding with NSAIDs increased and became

com-parable with figures in the literature. With regard to

over-the-counter use, nondifferential under-reporting has been

shown to reduce the measured effect of prescribed

medica-tions.

_{In our study, this would cause an underestimate of}

the effect of NSAIDs. However, in England, certain groups

receive free prescriptions, such as patients older than 65

years or those with certain chronic diseases, and these groups

have been shown to purchase far fewer medications over the

counter than those who have to pay for prescriptions.

When we restricted our analysis to those older than 65 years,

thereby reducing confounding by over-the-counter

medica-tions, we found only a small reduction in the estimated PAF

for comorbidity, but no change in PAF for NSAIDs. The

final area of under-reporting that could affect our study was

missing data for alcohol and smoking status, but these

variables were not strong confounders of the association

between comorbidity and bleeding and there was only a

minimal effect on the PAF of comorbidity when missing

data were imputed conditional on all available data and

socioeconomic status.

We therefore believe that potential under-reporting of

exposures does not explain the association we found

be-tween upper GIB and a general measure of comorbidity.

This suggests that comorbidity itself, or other factors not

included in our study that are associated with

comorbid-ity, might be causing the association. It is possible that

other medications not included in the study were

respon-sible for some of this association, however, we are not

aware of any additional prescribed or nonprescribed

med-ication that would fulfill the requirements of common

usage and a strong association with bleeding. Historically,

nongastrointestinal comorbidity itself was commonly

rec-ognized as a risk factor for upper GIB.

_{However, the}

concept of “stress ulceration” is no longer accepted, aside

from patients on ITU who are exposed to severe acute

physiological stresses from ventilation, coagulopathy,

liver failure, renal failure, septic shock, or nutritional

support.

_{The physiological effects from chronic}

comor-bidities in our study are unlikely to be as severe as those

that occur on ITU and, therefore, what we are describing

is likely to have a different mechanism than that seen in the

ITU setting. Many potential mechanisms for our observed

association can be hypothesized; for example, reduced

epi-thelial microperfusion in cardiac failure,

_{decreased oxygen}

levels in chronic obstructive pulmonary disease,

_poor

nutritional status in many diseases, or the platelet and

clot-ting dysfunction in end-stage renal failure.

_{However, it is}

unlikely that there is a single mechanism that accounts for

the association we found, but rather that multiple illnesses

and mechanisms have a cumulative effect. This was shown

by the graded effect of the Charlson Index and by

Table 6

, in

which no individual disease accounted for the magnitude of

the overall association with comorbidity.

Our findings contrast with current beliefs that the main

burden of bleeding in the general population comes from

known iatrogenic causes, such as NSAIDs prescribed for

analgesia or antiplatelet agents prescribed for cardiac and

cerebrovascular disease,

_{and that this burden would be}

reduced by increasing PPI use.

_{Instead, we have}

demon-strated that the extra contribution of these medications to

bleeding cases was not large after considering the

contri-Table 6. The Adjusteda_{Association of the Component Comorbidities of Charlson Index With Nonvariceal Bleeding}

Proportion of

cases exposed,% ORa

Lower 95% CI

Upper

95% CI PAF,b_%

Myocardial infarction 13.98 1.04 0.97 1.10 0.12 Congestive cardiac disease 19.90 1.49 1.41 1.58 1.95 Peripheral vascular disease 11.17 1.31 1.23 1.41 0.70

Dementia 8.98 1.40 1.30 1.50 1.00

Chronic pulmonary disease 31.80 1.11 1.06 1.16 1.10 Cerebrovascular disease 23.1 1.13 1.08 1.19 0.80 Rheumatological disease 10.13 1.06 0.99 1.13 0.17 Uncomplicated diabetes 17.88 1.01 0.96 1.06 0.04

Hemiplegia 4.73 1.79 1.62 1.97 0.67

Renal disease 14.42 1.71 1.61 1.82 1.74

Diabetes with complications 12.30 1.00 0.94 1.06 ⫺0.01

Any malignancy 13.11 1.21 1.14 1.28 0.78

Lymphoproliferative disorders 2.21 1.95 1.70 2.24 0.43 Metastatic solid tumour 5.89 2.35 2.14 2.57 1.29

HIV/AIDS 0.06 0.69 0.31 1.55 ⫺0.00

HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome.

a_{Adjusted for all other variables in this Table andTable 2and matched for year, practice, and sex.}

b_{Sequential population attributable fractions: The estimates in each row are calculated separately conditional on all the other variables in the}

model. They should therefore not be interpreted as summing over the column to 100%. Sequential PAF estimates the additional proportion of nonvariceal bleeding cases attributable to each risk factor after cases attributable to all the other risk factors in the model have been removed.

CLINICAL

butions of other risk factors present in the population.

Therefore, simply increasing PPI prescriptions in patients

on high-risk medications might not have as large an

impact as previously thought.

In conclusion, the largest measurable burden of upper

gastrointestinal hemorrhage in this study was attributed

to nongastrointestinal comorbidity. In a proportion of

patients, a bleed is an indicator of the burden of their

comorbidity, and recognizing this will help guide

man-agement, particularly in the absence of modifiable

gastro-intestinal risk factors. However, our finding also explains

why the incidence of nonvariceal bleeding is likely to

remain high in an aging population, thereby necessitating

continued acute gastroenterology service provision.

Supplementary Material

Note: To access the supplementary material

accompanying this article, visit the online version of

Gastroenterology

at

www.gastrojournal.org

, and at

http://

dx.doi.org/10.1053/j.gastro.2013.02.040

.

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Received November 2, 2012. Accepted February 27, 2013.

Reprint requests

Address requests for reprints to: Colin Crooks, MSc, The University of Nottingham, Division of Epidemiology and Public Health, Clinical

Sciences Building, Nottingham City Hospital, Nottingham, NG5 1PB UK. e-mail:colin.crooks@nottingham.ac.uk; fax:ⴙ0115 823 0214.

Conflicts of interest

This author discloses the following: Timothy Richard Card is married to an employee of Takaeda Pharmaceuticals (recently changed jobs from AstraZeneca). The remaining authors disclose no conflicts.

Funding

This work was supported by personal fellowships: Colin Crooks is a research fellow and gastroenterology trainee supported by a Medical Research Council Population Health Scientist fellowship (grant number G0802427), Tim Card is a Clinical Associate Professor and Consultant Gastroenterologist supported by Nottingham University and the National Health Service, and Joe West is a Clinical Associate Professor, Reader in Epidemiology, and Consultant Gastroenterologist supported by a University of Nottingham/Nottingham University Hospitals NHS Trust Senior Clinical Research fellowship. These funding bodies had no role in the study design, or the collection, analysis, or interpretation of data.

CLINICAL

Supplementary Table 1. Proportion of Cases Exposed 2 Months Before Bleed Date or Match Date Stratified by Coded Peptic Ulcer

Peptic ulcer coded,n

Exposed,

%

No peptic ulcer coded,n

Exposed,

%

Charlson index⫹

No comorbidity 883 18.3 2557 22.2

Single comorbidity 916 19.0 2306 20.0

Multiple or severe 3024 62.7 6669 57.8

Gastrointestinal

Cirrhosis—none coded 4753 98.5 11,251 97.6

Cirrhosis only 17 0.4 46 0.4

Cirrhosis—varices 8 0.2 57 0.5

Cirrhosis—ascites 32 0.7 140 1.2

Cirrhosis—encephalopathy 13 0.3 38 0.3

Gastritis, duodenitis, or esophagitis 710 14.7 2341 20.3

Peptic ulcer 864 17.9 988 8.6

Helicobacter pylori 162 3.4 447 3.9

Angiodysplasia 1 0.0 5 0.0

Mallory-Weiss syndrome 11 0.2 85 0.7

Crohn’s disease 19 0.4 95 0.8

GI cancer 254 5.3 920 8.0

Lifestyle

Alcohol—not coded 3299 68.4 7727 67.0

Alcohol—non-drinker 97 2.0 278 2.4

Alcohol—ex-drinker 20 0.4 44 0.4

Alcohol—mentioned 284 5.9 693 6.0

Alcohol—over limits 1105 22.9 2658 23.0

Alcohol—complications 18 0.4 132 1.1

Smoking—not coded 2753 57.1 6434 55.8

Smoking—nonsmoker 646 13.4 1686 14.6

Smoking—ex-smoker 288 6.0 600 5.2

Smoking—passive 405 8.4 1050 9.1

Smoking—current 731 15.2 1762 15.3

Medications

Aspirin 1831 38.0 3561 30.9

NSAIDs 1431 29.7 2467 21.4

COX II inhibitors 222 4.6 383 3.3

Clopidogrel 198 4.1 470 4.1

Oral steroids 428 8.9 1150 10.0

Anticoagulants 427 8.9 1190 10.3

SSRIs 460 9.5 1565 13.6

Other diagnoses

Aortic stenosis 125 2.6 225 2.0

Repair of AAA 36 0.7 79 0.7

Dialysis 30 0.6 58 0.5

Confounders

Previous upper GI procedure 817 16.9 2621 22.7

Previous PPI use 906 18.8 3679 31.9

Age (median and interquartile range) 75 64–83 72 54–82

AAA, Abdominal aortic aneurysm; SSRI, selective serotonin reuptake inhibitors.

a_{Nongastrointestinal comorbidity is catogorized as: Charlon Index⫽0 is no comorbidity, Charlson Index⫽1 single or comorbidity, and Charlson}

Supplementary Table 2. The Association of Medications With Upper Gastrointestinal Bleeding After Excluding Patients With Nonmedication Risk Factors (Age, Year, Practice, and Sex Matched)

Crude OR

Adjusteda

OR

Lower 95% CI

Upper 95% CI

Aspirin 2.39 1.73 1.60 1.87 NSAIDs 2.80 1.78 1.64 1.93 COX II inhibitors 2.59 1.50 1.23 1.83 Clopidogrel 7.30 2.15 1.70 2.73 Oral steroids 1.23 1.23 1.08 1.41 Anticoagulants 4.83 2.26 1.99 2.57 SSRIs 2.78 1.52 1.34 1.71

SSRI, selective serotonin reuptake inhibitors.

a_{Adjusted for all other variables in Table and inFigure 1.}

Supplementary Table 3. Sequential Population Attributable Fractions for Nonvariceal Upper Gastrointestinal Hemorrhage (NSAIDS Prescribed Between 1 Month and 1 Year Before Bleed)

Sequential population attributable

fractions,a,b_%

Nongastrointestinal comorbidity 19.92 Gastrointestinal

Cirrhosis 0.48

Gastritis, duodenitis, or esophagitis 1.86

Peptic ulcer 2.01

Helicobacter pylori ⫺0.06

Angiodysplasia 0.01 Mallory-Weiss syndrome 0.28 Crohn’s disease 0.14

GI cancer 1.09

Lifestyle

Alcohol use 2.83

Smoking 0.93

Medications

Aspirin 2.91

NSAIDs 3.18

COX II inhibitors 0.36

Clopidogrel 0.32

Oral steroids 0.58 Anticoagulants 1.11

SSRIs 1.60

Other diagnoses

Aortic stenosis 0.16 Repair of aorta 0.02

Dialysis 0.06

SSRI, selective serotonin reuptake inhibitors.

a_{Age, year, practice, and sex matched and adjusted for PPI use,}

previous upper gastrointestinal procedures, and age.

b_{The estimate in each row are calculated separately conditional on all}